Gastos Casa
24 abril, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 1/3/2023 | Uber | 3308 | Andrés | viaje lunes noche |
| 1/3/2023 | Comida | 7200 | Andrés | Frutos secos |
| 5/3/2023 | Comida | 62296 | Tami | NA |
| 6/3/2023 | Enceres | 110000 | Andrés | arreglo reja |
| 9/3/2023 | Forro cortina ducha | 2490 | Tami | NA |
| 4/3/2023 | Microondas regalo | 40000 | Tami | NA |
| 9/3/2023 | Comida | 106490 | Tami | Soul Bar |
| 9/3/2023 | Comida | 27642 | Tami | NA |
| 13/3/2023 | Comida | 51473 | Tami | NA |
| 13/3/2023 | Diosi | 20990 | Tami | Antiparasitario |
| 16/3/2023 | Vacunas Influenza | 19980 | Tami | NA |
| 20/3/2023 | Comida | 52314 | Tami | NA |
| 26/3/2023 | Comida | 24970 | Andrés | caramagnola |
| 28/3/2023 | Comida | 71805 | Tami | NA |
| 29/3/2023 | Electricidad | 42447 | Andrés | PAC ENEL 01686518 |
| 30/3/2023 | Netflix | 8320 | Tami | NA |
| 31/3/2023 | Comida | 13226 | Tami | NA |
| 31/3/2023 | Comida | 100000 | Andrés | wild foods |
| 31/3/2023 | Enceres | 15400 | Tami | Incoludido |
| 9/4/2023 | Gas | 67300 | Andrés | el de la derecha |
| 10/4/2023 | Comida | 61792 | Tami | NA |
| 17/4/2023 | Comida | 41602 | Tami | NA |
| 19/4/2023 | VTR | 21990 | Andrés | NA |
| 19/4/2023 | nacho | 55000 | Andrés | NA |
| 22/4/2023 | Comida | 19420 | Tami | NA |
| 23/4/2023 | Comida | 50617 | Tami | NA |
| 23/4/2023 | Crunchyroll | 49900 | Tami | NA |
| 23/4/2023 | Netflix | 5940 | Tami | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 6.1145e+08 2 6.3519 0.0019 **
## lag_depvar 8.3935e+10 1 1743.9039 <2e-16 ***
## Residuals 2.7434e+10 570
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 992.5793 13465.10 0.0182275
## 2-0 28175.416 22475.1884 33875.64 0.0000000
## 2-1 20946.578 17547.8530 24345.30 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 418 50409.68 15480.356
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1998.56927 4031.66597 -529.39500 2445.59364 -2952.47374 524.98251
## 8 9 10 11 12 13
## -5646.67890 -1198.05040 -3977.44426 -440.05884 -4958.64256 -1641.59760
## 14 15 16 17 18 19
## -931.71627 348.23832 -3265.18795 -407.01202 -2154.98532 6576.72901
## 20 21 22 23 24 25
## -1528.01414 -1210.85130 1470.93280 -1183.63548 234.87763 1697.89235
## 26 27 28 29 30 31
## -7091.64415 933.44441 8185.40877 443.15729 11.28768 -2376.55439
## 32 33 34 35 36 37
## 1590.67071 4592.72178 1162.79792 2428.80791 -1824.85491 4641.01926
## 38 39 40 41 42 43
## 4323.27488 -2242.90869 -2960.64867 -1102.36345 -10738.41297 7254.10481
## 44 45 46 47 48 49
## 2552.41388 1371.82760 8114.52155 723.21900 6563.76058 6768.29779
## 50 51 52 53 54 55
## -5811.25099 -4753.97055 -5040.29129 -7929.32065 6101.24542 -4079.70855
## 56 57 58 59 60 61
## -4911.31787 3823.77037 873.62593 -41.24943 134.27076 -5002.73667
## 62 63 64 65 66 67
## 18103.72585 3684.86696 -3594.39452 5957.86179 7393.67371 14708.55128
## 68 69 70 71 72 73
## 1806.25532 -13107.60271 -1260.69898 4678.89977 -4852.66012 -4379.95437
## 74 75 76 77 78 79
## -10491.21763 2435.69636 -5417.89766 1029.26916 -6892.33782 500.32637
## 80 81 82 83 84 85
## -2393.05197 -2735.44686 -3977.35348 -590.73735 2266.52064 3728.99825
## 86 87 88 89 90 91
## 460.81468 -496.80940 184.32434 4292.02214 -1156.46178 1152.64641
## 92 93 94 95 96 97
## -2058.71257 -1046.33480 172.30578 270.95715 -7486.25694 2363.87967
## 98 99 100 101 102 103
## -8618.24916 -2984.02580 -4087.26266 -1792.11910 -1315.33420 3129.79575
## 104 105 106 107 108 109
## -2375.37929 2556.99516 -1181.49327 946.47051 2569.72028 -3160.36381
## 110 111 112 113 114 115
## -4739.05652 -880.45437 1874.43227 11674.98800 -1217.88102 2685.98846
## 116 117 118 119 120 121
## 4287.62077 3539.43804 -1055.34858 -4680.93217 -3709.32794 2319.97718
## 122 123 124 125 126 127
## -1724.11147 1342.11765 8864.66361 883.85312 165.77645 -2489.68108
## 128 129 130 131 132 133
## 2674.13636 7078.69921 1060.16082 -8453.92779 1759.53150 4150.86280
## 134 135 136 137 138 139
## -3135.90122 -1406.02852 -846.72428 -3876.41324 1172.90462 -499.98011
## 140 141 142 143 144 145
## -2919.03605 1703.49399 -1887.69334 -7841.42005 2001.81326 -3505.30408
## 146 147 148 149 150 151
## 2067.92473 -279.97884 1002.61125 -373.43769 1338.73443 1179.72365
## 152 153 154 155 156 157
## 3354.81898 -4851.48610 -1182.22449 -3246.49552 5936.29662 9749.70287
## 158 159 160 161 162 163
## -3336.43505 -4689.31143 3682.80017 293.63101 2800.58985 -5792.89098
## 164 165 166 167 168 169
## -6647.68782 4238.09327 17494.61204 3785.38157 -233.94282 -2287.65552
## 170 171 172 173 174 175
## -957.59429 3730.91701 -78.74351 -7929.90167 2981.43683 4452.72790
## 176 177 178 179 180 181
## 765.46213 8889.97749 -9084.60684 -3341.53805 -10626.14525 -11154.16684
## 182 183 184 185 186 187
## 1290.99493 9361.50897 -1327.06373 6029.18193 6675.00963 13294.99173
## 188 189 190 191 192 193
## 8599.57821 -3881.46962 2621.79561 10522.65359 -1470.61938 -2288.30590
## 194 195 196 197 198 199
## -10140.65494 -6258.40121 1319.66161 -5142.18814 -9717.86627 5438.74008
## 200 201 202 203 204 205
## -2991.22445 -1639.65187 -731.39548 6569.32854 9976.32497 696.20967
## 206 207 208 209 210 211
## 3039.65340 3216.69970 5906.92975 12966.44198 -5527.82970 -11160.41627
## 212 213 214 215 216 217
## -5564.88619 -10500.49026 -5012.93463 1582.29842 -12943.90925 16428.73689
## 218 219 220 221 222 223
## 7898.72860 1637.63708 26799.23796 12698.32036 7524.37670 14218.61140
## 224 225 226 227 228 229
## -3703.17313 -1557.93235 3942.71061 524.36317 2902.05424 9160.00737
## 230 231 232 233 234 235
## 6002.35587 -1726.69055 -1662.74136 9578.33839 -11338.81188 -7152.36224
## 236 237 238 239 240 241
## -8432.82190 -10015.88452 3139.86088 1427.85973 -8213.99727 -8924.23586
## 242 243 244 245 246 247
## 9143.21631 -7686.64741 2550.30917 -10226.43991 -4000.57398 1473.02685
## 248 249 250 251 252 253
## 1063.93582 -12247.14611 3686.64041 2122.28206 4281.59913 2217.90059
## 254 255 256 257 258 259
## -1071.13995 11226.10522 20992.98333 3349.28110 -4123.88855 4236.80109
## 260 261 262 263 264 265
## -1565.65109 3854.90088 -4731.56399 -10790.08604 -4652.25641 -454.70528
## 266 267 268 269 270 271
## -5120.04741 8837.00047 -4200.60663 4259.27911 -2027.99983 4504.40747
## 272 273 274 275 276 277
## 791.06035 7384.43233 -1318.71499 12111.08976 -4480.25440 1812.74400
## 278 279 280 281 282 283
## -286.36264 7932.41553 -4965.91731 -2653.19229 -11189.56979 -2616.09150
## 284 285 286 287 288 289
## 18707.33500 7869.73087 2828.95425 -535.11945 992.95292 6482.18739
## 290 291 292 293 294 295
## 6972.27021 -18677.11529 -11074.87766 -8069.50712 9712.29267 3140.35189
## 296 297 298 299 300 301
## -1103.68684 27477.20793 10176.85102 5017.87141 9632.69920 2975.27315
## 302 303 304 305 306 307
## -919.00518 8000.80982 -24187.11245 -3459.37246 -100.97949 -6890.51692
## 308 309 310 311 312 313
## -3898.22224 3006.80610 -9108.84884 -3151.61668 -8104.03606 1646.48142
## 314 315 316 317 318 319
## -3061.13108 2140.48195 -3984.02195 27542.48072 -619.24790 3390.12991
## 320 321 322 323 324 325
## 10928.29457 5692.76157 32484.01895 5239.92578 -20811.76791 1892.06940
## 326 327 328 329 330 331
## 1209.32380 -6367.24674 -1643.16115 -33177.22527 984.02132 -2186.70008
## 332 333 334 335 336 337
## 31.11952 -3034.76994 4223.66204 -290.64821 -6802.54501 -2967.04787
## 338 339 340 341 342 343
## -2039.38262 -7524.15027 4006.80082 -1210.49837 -1575.58618 -830.50478
## 344 345 346 347 348 349
## 341.36704 647.77674 -1451.74530 -9280.89753 -13049.13426 2466.35637
## 350 351 352 353 354 355
## -4162.35976 -3496.87411 -5817.01737 1913.85246 1550.01500 2919.34781
## 356 357 358 359 360 361
## -3600.87538 -353.86592 838.51273 7173.68663 436.10217 118.40982
## 362 363 364 365 366 367
## 2737.20479 -2597.70266 -726.29572 -8592.32587 -4473.73902 -6057.31461
## 368 369 370 371 372 373
## -4791.28375 -7091.04456 5179.43693 536.49164 7284.99706 -7472.37062
## 374 375 376 377 378 379
## -2099.15782 -3222.79894 -2300.36944 -12289.15893 2073.11660 -10462.13568
## 380 381 382 383 384 385
## 5869.64713 9509.55048 3296.24533 -2232.59439 1767.65286 6903.89746
## 386 387 388 389 390 391
## 11565.30181 -5656.54546 -5225.49662 -24.71217 8693.44112 1940.25521
## 392 393 394 395 396 397
## 11342.38136 -9765.71785 2882.11500 817.44034 664.85087 -553.05792
## 398 399 400 401 402 403
## -463.25574 -14387.67807 8633.43417 -1064.84190 -1252.37711 7105.58300
## 404 405 406 407 408 409
## -7809.25592 -1167.79154 -2397.05444 -5678.46807 -2710.20482 -3761.64953
## 410 411 412 413 414 415
## -8593.50725 6303.16515 1809.35493 -7205.21300 -7519.99587 14398.39662
## 416 417 418 419 420 421
## 3982.89653 4650.39341 -7885.56767 -4591.42509 -2443.96761 2980.90129
## 422 423 424 425 426 427
## -13849.95041 -2621.63694 -8924.26669 3194.98422 7158.97570 6751.97805
## 428 429 430 431 432 433
## -3817.69431 -3950.74246 -4550.20128 -1614.67916 -5535.42121 -6446.88582
## 434 435 436 437 438 439
## -5765.88370 -1205.79572 -658.95662 -4785.83153 2772.93952 5028.64223
## 440 441 442 443 444 445
## -4871.95956 -1972.81008 1761.82864 -3653.55995 3019.75437 -6396.40157
## 446 447 448 449 450 451
## -11926.89126 -4322.93142 9836.62427 -1843.21035 4943.51158 -5682.66792
## 452 453 454 455 456 457
## -934.51822 572.24847 3214.25422 -12082.17700 3557.86260 -6513.07855
## 458 459 460 461 462 463
## 6711.89457 3200.29657 2693.16053 -3661.79582 2276.39199 174.90060
## 464 465 466 467 468 469
## 1974.10562 -341.67481 3528.88282 -2462.66291 5981.84069 -6766.83985
## 470 471 472 473 474 475
## -2786.46011 -2024.70966 -4480.46511 3182.81967 7984.44890 -5831.55481
## 476 477 478 479 480 481
## 1670.93619 -5991.77540 -2655.41647 2202.78939 -12738.35019 -9562.04411
## 482 483 484 485 486 487
## -1007.17301 217.48765 -763.59649 -1142.39739 -9384.72130 11295.77089
## 488 489 490 491 492 493
## 6435.99233 7619.47030 -5240.08666 5561.92907 9485.57956 6243.14941
## 494 495 496 497 498 499
## -13288.01440 -10381.09586 -3254.46445 -916.67367 -332.97734 -7432.80892
## 500 501 502 503 504 505
## 806.55597 4485.55478 5707.75145 859.37798 278.87756 -7041.27993
## 506 507 508 509 510 511
## 767.71851 -4849.77907 2031.72753 -1096.34520 -7957.40462 -401.34981
## 512 513 514 515 516 517
## -2472.57402 -385.03013 1535.68066 -9291.24280 -7561.75557 24490.28577
## 518 519 520 521 522 523
## 10030.47168 6081.56206 -5137.55819 2992.51378 17210.65637 11661.69411
## 524 525 526 527 528 529
## -23966.13199 -4880.96312 -3554.11527 4752.41967 -175.63126 -10921.49110
## 530 531 532 533 534 535
## 4573.47418 14093.54674 -4792.90931 4555.67453 5735.65031 -1611.62213
## 536 537 538 539 540 541
## -4363.75651 -6897.04731 -1922.04714 8503.41052 309.73108 -7958.74926
## 542 543 544 545 546 547
## 1999.45159 -414.43561 552.54622 -10843.59271 -10873.24015 2231.43645
## 548 549 550 551 552 553
## 7197.29338 -1121.88868 1033.25541 -7523.78142 8762.22463 1104.97116
## 554 555 556 557 558 559
## -11746.65903 9358.85380 8854.12622 293.26250 5044.55569 -3385.48582
## 560 561 562 563 564 565
## 14294.84887 21682.33702 -6288.42614 -9508.04569 6945.29218 395.20659
## 566 567 568 569 570 571
## 3622.33392 -7210.36055 -17140.26842 6834.96598 6612.14970 2082.79471
## 572 573 574 575
## 3280.38307 1953.21993 -1981.00906 14901.53818
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17270.72 20107.33 24345.54 24064.55 26409.19 23751.73 24465.39 19715.19
## 10 11 12 13 14 15 16 17
## 19452.73 16805.34 17579.93 14321.45 14372.43 15034.62 16724.90 15051.15
## 18 19 20 21 22 23 24 25
## 16081.99 15457.84 22514.01 21601.42 21083.21 22966.21 22294.69 22944.82
## 26 27 28 29 30 31 32 33
## 24783.93 18734.84 20454.59 28262.84 28320.28 27994.41 25632.62 27029.85
## 34 35 36 37 38 39 40 41
## 30858.63 31205.76 32609.71 30129.55 34119.73 37315.91 34382.93 31205.65
## 42 43 44 45 46 47 48 49
## 30057.70 20672.18 28163.01 30590.46 31675.62 38488.35 37984.81 42629.70
## 50 51 52 53 54 55 56 57
## 46850.25 39575.26 34163.86 29205.03 22374.90 28641.57 25234.89 21546.23
## 58 59 60 61 62 63 64 65
## 25938.23 27193.11 27489.01 27899.31 23785.56 40315.28 42152.39 37416.00
## 66 67 68 69 70 71 72 73
## 41607.33 46504.73 57133.32 55154.46 40452.41 37967.53 40974.23 35295.53
## 74 75 76 77 78 79 80 81
## 30764.65 21502.59 24692.18 20633.02 22711.34 17625.82 19633.77 18863.16
## 82 83 84 85 86 87 88 89
## 17894.50 15970.59 17243.62 20838.29 25239.61 26225.81 26250.68 26865.12
## 90 91 92 93 94 95 96 97
## 30974.89 29809.78 30805.43 28877.05 28079.84 28446.61 28851.69 22452.98
## 98 99 100 101 102 103 104 105
## 25456.82 18513.17 17373.55 15421.55 15720.19 16395.06 20851.09 19938.00
## 106 107 108 109 110 111 112 113
## 23436.06 23226.82 24896.71 27762.79 25270.20 21726.88 22001.28 24637.73
## 114 115 116 117 118 119 120 121
## 35461.88 33661.44 35492.09 38479.28 40427.92 38124.93 32965.19 29320.17
## 122 123 124 125 126 127 128 129
## 31395.25 29681.60 30858.76 38430.29 38074.08 37139.11 34014.29 35788.87
## 130 131 132 133 134 135 136 137
## 41166.70 40609.07 31843.47 33103.57 36281.47 32705.46 31098.72 30187.13
## 138 139 140 141 142 143 144 145
## 26756.95 28166.12 27936.61 25631.51 27648.41 26278.28 19904.19 22923.45
## 146 147 148 149 150 151 152 153
## 20758.22 23724.26 24262.25 25846.72 26028.12 27676.13 28972.04 31992.91
## 154 155 156 157 158 159 160 161
## 27479.94 26745.64 24309.99 30182.15 41356.86 39693.31 37068.06 42069.65
## 162 163 164 165 166 167 168 169
## 43472.98 46876.18 42358.97 37683.62 43088.67 59330.19 61534.09 59954.08
## 170 171 172 173 174 175 176 177
## 56791.59 55196.80 57889.31 56917.04 49237.85 52050.84 55779.54 55815.59
## 178 179 180 181 182 183 184 185
## 62917.89 53455.54 50218.57 41061.45 32632.29 36127.49 46193.35 45651.39
## 186 187 188 189 190 191 192 193
## 51581.99 57305.58 68048.42 73311.61 67029.78 67222.49 74266.48 69959.02
## 194 195 196 197 198 199 200 201
## 65498.51 54782.40 48834.77 50253.76 45864.87 38062.83 44463.65 42697.65
## 202 203 204 205 206 207 208 209
## 42336.97 42813.53 49582.25 58438.36 58069.35 59787.73 61437.36 65214.42
## 210 211 212 213 214 215 216 217
## 74645.69 66757.99 54991.03 49619.92 40649.79 37618.84 40720.91 30778.26
## 218 219 220 221 222 223 224 225
## 47688.56 54982.08 55880.62 78561.25 86028.34 88024.10 95587.17 86571.79
## 226 227 228 229 230 231 232 233
## 80592.58 80176.07 76838.52 76003.14 80722.50 82081.69 76537.88 71768.66
## 234 235 236 237 238 239 240 241
## 77401.24 64098.79 56164.96 48145.60 39788.42 43964.71 46109.43 39584.52
## 242 243 244 245 246 247 248 249
## 33287.64 43531.79 37800.12 41721.15 34013.86 32724.54 36366.21 39179.57
## 250 251 252 253 254 255 256 257
## 30043.22 35959.15 39746.40 44921.81 47630.00 47124.47 57387.02 74819.00
## 258 259 260 261 262 263 264 265
## 74634.75 67970.34 69446.65 65681.53 67122.28 60903.23 50217.83 46259.99
## 266 267 268 269 270 271 272 273
## 46468.62 42589.86 51361.18 47648.15 51779.43 49903.02 53955.23 54250.14
## 274 275 276 277 278 279 280 281
## 60245.14 57888.20 67525.11 61472.54 61681.79 60037.01 65758.49 59512.34
## 282 283 284 285 286 287 288 289
## 56089.00 45680.23 44082.95 61250.98 66760.47 67168.41 64595.62 63686.38
## 290 291 292 293 294 295 296 297
## 67672.44 71568.12 52635.45 42774.36 36807.71 47090.65 50320.40 49437.65
## 298 299 300 301 302 303 304 305
## 73543.86 79467.13 80132.30 84727.58 82932.86 77981.62 81435.54 56427.80
## 306 307 308 309 310 311 312 313
## 52702.84 52383.80 46197.08 43416.91 47006.85 39586.76 38313.61 32895.38
## 314 315 316 317 318 319 320 321
## 36665.85 35850.23 39667.45 37659.38 63349.82 61199.01 62816.56 70784.95
## 322 323 324 325 326 327 328 329
## 73163.41 98550.36 96934.05 72854.07 71656.39 70019.82 62001.45 59134.37
## 330 331 332 333 334 335 336 337
## 29194.41 32868.27 33306.17 35617.48 34960.77 40706.36 41777.97 37043.19
## 338 339 340 341 342 343 344 345
## 36260.53 36386.72 31723.06 37699.78 38360.73 38618.22 39490.78 41270.08
## 346 347 348 349 350 351 352 353
## 43085.32 42837.90 35808.71 26411.50 31736.36 30601.59 30193.16 27818.43
## 354 355 356 357 358 359 360 361
## 32479.99 36220.37 40667.45 38863.15 40118.77 42249.31 49617.18 50165.73
## 362 363 364 365 366 367 368 369
## 50366.65 52820.70 50313.44 49760.04 42432.45 39639.60 35830.71 33617.62
## 370 371 372 373 374 375 376 377
## 29689.99 36950.94 39229.43 47085.80 41079.73 40528.94 39071.66 38606.16
## 378 379 380 381 382 383 384 385
## 29507.60 34088.71 27166.07 35355.02 45649.90 49202.17 47481.92 49466.25
## 386 387 388 389 390 391 392 393
## 55663.41 65113.83 58350.21 52838.86 52568.56 59920.89 60442.33 69079.00
## 394 395 396 397 398 399 400 401
## 58224.88 59785.99 59347.72 58833.49 57325.97 56092.11 42899.57 51453.56
## 402 403 404 405 406 407 408 409
## 50457.66 49427.70 55805.40 48375.36 47689.05 46021.90 41715.06 40550.08
## 410 411 412 413 414 415 416 417
## 38621.08 32736.98 40580.79 43496.36 38188.28 33294.60 48111.53 51942.18
## 418 419 420 421 422 423 424 425
## 55857.00 48353.85 44690.68 43371.53 46944.81 35406.49 35136.70 29416.59
## 426 427 428 429 430 431 432 433
## 34985.88 43282.88 50149.69 46927.03 44006.49 40942.96 40831.56 37322.31
## 434 435 436 437 438 439 440 441
## 33474.88 30719.08 32289.39 34131.97 32143.92 36992.21 43174.96 39939.24
## 442 443 444 445 446 447 448 449
## 39646.31 42641.70 40535.53 44510.40 39774.75 30839.93 29681.66 40996.92
## 450 451 452 453 454 455 456 457
## 40679.63 46310.10 41962.23 42310.61 43925.17 47629.75 37541.14 42372.65
## 458 459 460 461 462 463 464 465
## 37812.68 45353.99 48861.13 51472.08 48213.61 50545.81 50746.61 52487.25
## 466 467 468 469 470 471 472 473
## 51986.69 54919.66 52257.73 57290.41 50575.03 48194.71 46786.04 43422.75
## 474 475 476 477 478 479 480 481
## 47165.12 54601.13 49048.49 50745.49 45553.42 43938.35 46760.92 36213.90
## 482 483 484 485 486 487 488 489
## 29799.03 31661.51 34348.31 35832.83 36795.15 30459.23 42943.58 49579.39
## 490 491 492 493 494 495 496 497
## 56384.66 51115.50 55930.85 63536.56 67334.01 53640.67 44253.04 42285.25
## 498 499 500 501 502 503 504 505
## 42607.26 43395.52 37902.44 40292.59 45574.68 51235.48 51942.55 52052.71
## 506 507 508 509 510 511 512 513
## 45777.71 47112.78 43385.70 46131.06 45797.98 39536.78 40663.72 39841.89
## 514 515 516 517 518 519 520 521
## 40943.46 43573.81 36440.18 31736.86 55538.96 63669.72 67309.27 60712.63
## 522 523 524 525 526 527 528 529
## 62047.20 75583.02 82534.13 57576.25 52465.12 49171.58 53534.49 53042.63
## 530 531 532 533 534 535 536 537
## 43262.24 48235.74 60849.77 55390.75 58775.92 62749.05 59812.47 54861.48
## 538 539 540 541 542 543 544 545
## 48347.76 47008.59 54916.55 54667.89 47255.26 49470.72 49298.03 49989.31
## 546 547 548 549 550 551 552 553
## 40672.67 32538.42 36864.28 44951.03 44748.74 46448.35 40480.20 49460.03
## 554 555 556 557 558 559 560 561
## 50611.09 40427.86 49933.73 57767.59 57134.87 60719.34 56502.15 68219.38
## 562 563 564 565 566 567 568 569
## 84846.57 74974.05 63579.71 67982.65 66113.95 67296.22 58897.27 42945.32
## 570 571 572 573 574 575
## 49928.14 55811.49 56989.90 59057.78 59702.44 56839.46
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8386
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 6.351942 0.54936 3.298483
## t2* 1743.903939 24.80619 225.173987
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.23218 6.485686 12.92429
## 2 lag_depvar 1420.04730 1754.400804 2154.96538Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Apr 24 00:36:20 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Apr 24 00:36:30 2023
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## =-=-=-=-= Iteration 8000 Mon Apr 24 00:37:00 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Apr 24 00:37:10 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Apr 24 00:37:21 2023
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## =-=-=-=-= Iteration 14000 Mon Apr 24 00:37:31 2023
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## =-=-=-=-= Iteration 16000 Mon Apr 24 00:37:41 2023
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 0.000000 | 5.410333 | 5.629750 | 6.5222308 |
| Comida | 402.177667 | 310.278417 | 314.087500 | 345.1240256 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 27.629000 | 47.072333 | 38.297667 | 32.0329744 |
| Enceres | 44.300000 | 20.086417 | 17.443792 | 26.1460256 |
| Farmacia | 6.660000 | 1.831667 | 7.913875 | 9.7013846 |
| Gas/Bencina | 23.433333 | 44.325000 | 28.954333 | 24.9308718 |
| Diosi | 16.193333 | 31.180667 | 41.934250 | 38.4732821 |
| donaciones/regalos | 0.000000 | 0.000000 | 7.170083 | 7.0440769 |
| Electrodomésticos/ Mantención casa | 0.000000 | 3.944000 | 30.269500 | 21.2684103 |
| VTR | 7.330000 | 25.156667 | 22.121792 | 20.0584103 |
| Netflix | 5.546667 | 7.151583 | 7.090167 | 7.3274615 |
| Otros | 0.000000 | 3.151083 | 1.575542 | 0.9695641 |
| Total | 533.270000 | 499.588167 | 522.488250 | 539.5987179 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1955, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-05-09 00:04:58 sería de: 37.333 pesos// Percentil 95% más alto proyectado: 40.851,84
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36124.97 | 36115.65 |
| Lo.80 | 36265.61 | 36337.69 |
| Point.Forecast | 37332.60 | 39863.15 |
| Hi.80 | 39275.87 | 44647.56 |
| Hi.95 | 40345.18 | 47180.28 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.2715 1006.339
## s.e. 0.1419 32.068
##
## sigma^2 = 28637: log likelihood = -326.53
## AIC=659.05 AICc=659.57 BIC=664.79paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2392 627.1565 12.4935
## s.e. 0.1450 365.6547 11.9910
##
## sigma^2 = 28663: log likelihood = -326.01
## AIC=660.03 AICc=660.92 BIC=667.68forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 789.5902 | 661.7235 | 706.4674 |
| Lo.80 | 907.8807 | 781.0071 | 820.3702 |
| Point.Forecast | 1131.3367 | 1006.3390 | 1035.5378 |
| Hi.80 | 1354.7927 | 1231.6709 | 1293.3154 |
| Hi.95 | 1473.0832 | 1350.9545 | 1429.7746 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.27 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.1 xts_0.13.1
## [13] forecast_8.21 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.0 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.2 forcats_1.0.0
## [22] dplyr_1.1.2 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
## [28] sjPlot_2.8.14 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.8.1 httr_1.4.5
## [34] readxl_1.4.2 zoo_1.8-12 stringr_1.5.0
## [37] stringi_1.7.12 DataExplorer_0.8.2 data.table_1.14.8
## [40] reshape2_1.4.4 fUnitRoots_4021.80 plyr_1.8.8
## [43] readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 igraph_1.4.2 lazyeval_0.2.2
## [7] splines_4.1.2 crosstalk_1.2.0 digest_0.6.31
## [10] htmltools_0.5.5 fansi_1.0.4 ggfortify_0.4.16
## [13] magrittr_2.0.3 tzdb_0.3.0 modelr_0.1.11
## [16] vroom_1.6.1 timechange_0.2.0 anytime_0.3.9
## [19] tseries_0.10-53 colorspace_2.1-0 xfun_0.39
## [22] crayon_1.5.2 jsonlite_1.8.4 lme4_1.1-32
## [25] glue_1.6.2 r2d3_0.2.6 gtable_0.3.3
## [28] emmeans_1.8.5 sjstats_0.18.2 sjmisc_2.8.9
## [31] car_3.1-2 quantmod_0.4.22 abind_1.4-5
## [34] mvtnorm_1.1-3 DBI_1.1.3 ggeffects_1.2.1
## [37] Rcpp_1.0.10 viridisLite_0.4.1 xtable_1.8-4
## [40] performance_0.10.3 bit_4.0.5 htmlwidgets_1.6.2
## [43] timeSeries_4021.105 gplots_3.1.3 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.5 dbplyr_2.3.2
## [52] janitor_2.2.0 utf8_1.2.3 tidyselect_1.2.0
## [55] labeling_0.4.2 rlang_1.1.0 munsell_0.5.0
## [58] cellranger_1.1.0 tools_4.1.2 cachem_1.0.7
## [61] cli_3.6.1 generics_0.1.3 sjlabelled_1.2.0
## [64] broom_1.0.4 evaluate_0.20 fastmap_1.1.1
## [67] yaml_2.3.7 knitr_1.42 bit64_4.0.5
## [70] caTools_1.18.2 forge_0.2.0 nlme_3.1-153
## [73] slam_0.1-50 xml2_1.3.3 tokenizers_0.3.0
## [76] compiler_4.1.2 rstudioapi_0.14 curl_5.0.0
## [79] bslib_0.4.2 highr_0.10 fBasics_4022.94
## [82] Matrix_1.5-4 its.analysis_1.6.0 nloptr_2.0.3
## [85] urca_1.3-3 vctrs_0.6.1 pillar_1.9.0
## [88] lifecycle_1.0.3 networkD3_0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 estimability_1.4.1 bitops_1.0-7
## [94] insight_0.19.1 R6_2.5.1 KernSmooth_2.23-20
## [97] janeaustenr_1.0.0 codetools_0.2-18 gtools_3.9.4
## [100] boot_1.3-28 MASS_7.3-54 assertthat_0.2.1
## [103] rprojroot_2.0.3 withr_2.5.0 fracdiff_1.5-2
## [106] bayestestR_0.13.1 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4022.108 minqa_1.2.5
## [112] snakecase_0.11.0 rmarkdown_2.21 carData_3.0-5
## [115] TTR_0.24.3 base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))